Curvature-Driven Dynamic Blade Tip Clearance Measurement Using Microwave Sensor

Abstract

As a core parameter for performance evaluation of rotating blades and turbines, high-precision noncontact measurement of blade tip clearance (BTC) faces severe challenges. Microwave sensing is promising for BTC measurement. However, static BTC measurements suffer from DC offsets, nonlinear amplitude modulation, and harmonic distortion, and dynamic clearance extraction is degraded by critical phase mutations. To address these issues, this article proposes a curvature-feature-driven correction algorithm based on analytic geometry, namely curvature-adaptive dynamic correction (CADC). CADC provides a parameter-free correction framework that combines a curvature-driven bidirectional compensation mechanism with a curvature-weighted covariance correction matrix, thereby mitigating the above interferences and phase mutations. Simulations verify robust calibration under nonlinear amplitude modulation and high-order harmonic interference, outperforming several state-of-the-art microwave BTC methods. Experiments show that a microwave sensor system with CADC achieves an average static displacement error of $4.150~\mu $ m over a 0–10 mm clearance range. For dynamic BTC measurement, the average absolute error per blade is $2.12~\mu $ m with an average repeatability error of $1.23~\mu $ m, and stable performance is maintained up to 3000 r/min.